Phylogenetic Tree Generation using Different Scoring Methods

Size: px
Start display at page:

Download "Phylogenetic Tree Generation using Different Scoring Methods"

Transcription

1 International Journal of Computer Applications ( ) Phylogenetic Tree Generation using Different Scoring Methods Rajbir Singh Associate Prof. & Head Department of IT LLRIET, Moga Sinapreet Kaur Student of M.Tech Department of CSE LLRIET, Moga Dheeraj Pal Kaur Assistant Prof. (ECE) Department of ECE LLRIET, Moga ABSTRACT Data Mining is a branch of knowledge discovery in the field of research and development. The biological data is available in different formats and is comparatively more complex. Knowledge discovery from these large and complex databases is the key problem of this era. Data mining and machine learning techniques are needed which can scale to the size of the problems and can be customized to the application of biology. Hierarchical Clustering is the one of the main techniques for data mining. Phylogeny is the evolutionary history for a set of evolutionary related species. One approach on determining the evolutionary histories of a dataset are scoring based methods. There are number of different distance based methods of which two are details with here: the UPGMA (Unweighted Pair Group Method using Arithmetic average) and Neighbor Joining. A method for construction of distance based phylogenetic tree using hierarchical clustering is proposed and implemented on different rice varieties. The sequences are downloaded from NCBI databank. Evolutionary distances are calculated using jukes cantor distance method. Multiple sequence alignment is applied on different datasets. Trees are constructed for different datasets from available data using both the distance based methods and pruning technique. SNAP calculates synonymous and nonsynonymous substitution rates based on a set of codon aligned nucleotide sequences. The DNA Multiple sequences to calculate the GC content of eukaryotes, molecular weight, melting temperature and tree information. Extractions of closely related varieties are performed by applying threshold condition. Then, final tree is constructed using these closely related rice varieties. Keywords Data Mining, DNA, Phylogenetics 1. INTRODUCTION DNA or deoxyribonucleic acid is a molecule that encodes the genetic instructions which are used in the development of all living organisms. Along with RNA and proteins, DNA is one of the three major macromolecules essential for all known living species. Genetic information is encoded in the form of sequence of nucleotides as guanine (G), adenine (A), thymine (T), and cytosine(c). DNA is organized into long structures called chromosomes. Like DNA, RNA is also vital for living beings. The bases in RNA are adenine, guanine, uracil and cytosine. RNA is very similar to DNA All of the genetic information of any living species is stored in deoxyribonucleic acid (DNA) and ribonucleic acid (RNA), which are polymers of four simple nucleic acid units which are called nucleotides. Phylogenetics is the study that helps to find out the relationship among different group of species. The relationship is discovered by moleculer sequencing data. A phylogenetic tree is a branching diagram which shows the evolutionary relationship among different species. Gene phylogeny represents the evolutionary relationship which is derived from genes or protein sequences. Species phylogeny represents the evolutionary path of the species. Reconstruction of phylogenetic relationship using a DNA, RNA or amino acid sequences is a hierarchal process.. Early approaches were based on single processor computers. and phylogenetics is based on parallel and distributed computing. In general phylogenetic trees represents the relationships between taxa. There are two different methods for construction of phylogenetic tree that are distance based tree construction method and character based tree construction method. These two categories both offer a vast variety of options when constructing trees in two different directions. Trees with scaled edges are called phylograms while with non scale edges are called cladograms.. METHODOLOGY The methodology for the work involves the use of distance based methods for phylogenetic tree construction. MATLAB and SNAP are the software tools used. Out of different Data Mining techniques, Hierarchical clustering is used. The data is taken from the databank of NCBI. SNAP (Synonymous Non- Synonymous Analysis Program) calculates synonymous and non-synonymous substitution rates based on a set of codon aligned nucleotide sequences and calculates pair wise synonymous and non-synonymous distances. If changes in the nucleotide sequences which does not change the sequences of amino acid of a proteins is synonymous substitution and if produce changes in amino acid sequences is termed as non synonymous substitution. Any biological information can be used to find out evolutionary relationship among taxas is called phylogenetic information maker. For this DNA sequences is used. Then multiple sequence alignment is done. For constructing DNA phylogenies, Jukes-cantor is used.. In this cluster-based methods are used. After selecting the appropriate methods and steps for tree construction, tree is constructed. Data used is taken from NCBI. sequences that are used, entered through the various formats. The various format available are: Plain Text format, FASTA format, GENBANK and Genetic Computer Group format (GCG). FASTA format is used for this problem and it is simple and easy to understand. Therefore DNA sequences are used and taken from NCBI in FASTA format for this work. 38

2 International Journal of Computer Applications ( ).1 Distance Measuring For constructing a phylogenetic tree based on data from protein or nucleotide sequence comparisons, first do a multiple alignment for the sequences and after that calculate distance measure of all taxa. Both methods use pair wise distances for determining the tree, here define distances d ij between each pair of sequences i, j in the given dataset.. Jukes Cantor method The Jukes and Cantor model is a model which computes probability of substitution from one state to another. From this method, also derive a formula for computing the distance between sequences. The main idea behind this method is the assumption that probability of changing from one state to a different state is always equal. As well, it is assumed that the different sites are independent. The evolutionary distance between two species is given by the following formula. 3 4 N ln 1 4 d d 4 3 N N d is the number of mutations between the two sequences and N is the nucleotide length..3 Molecular Clock For assigning branch lengths to phylogenetic tree, must consider whether the evolutionary rate is constant. The determination of a phylogenetic tree for the evolution of species from amino acid or nucleotide sequence comparisons depends on measurements and assumptions concerning the rate of evolutionary change is molecular clock..4 Mutation Rate Mutation are exchange of nucleotide or any insertion or deletion of nucleotide.the mutation rate is a measure of the rate at which various types of mutations occur during some unit of time.it may be small or large scale insertions or deletions..5 Functional constraint Natural selection occurs when changes to gene that diminish the ability to survive of an organism and reproduce, are removed from gene pool. The portion of genes which are very important for survival of organism is under functional constraint, and changes occur very slowly over the evolution..7 Neighbor joining method Neighbor Joining (NJ) works like UPGMA method. It creates a new distance matrix at each step, and creates the tree based on the matrices. NJ does not construct clusters and directly calculates distances to internal nodes,that is the difference between UPGMA and NJ methods. The first step of the NJ method is that to create a matrix with the Hamming distance between each node. The minimal distance that is calculated is then used to calculate the distance from the two nodes to the node that directly links them. Therefore a new matrix is calculated and the new node is substituted for the original nodes that are now joined. Neighbor Joining works even if the lengths are not additive but there is no guarantee that the tree is correct. D i, j, k D D D i, k j, k i, j 3. RESULTS AND DISCUSSION Phylogenetic Tree Construction is one of the most important goals pursued by bioinformatics. To generate a phylogenetic tree the main computational problems are threefold. Firstly, to determine, and compute, a distance metric between every genomic sequence. Secondly, to perform hierarchical clustering on the given datasets, utilizing the distance metric computations. Two distance matrices that are compatible input for Jukes Cantor model are created, based on either the ds or dn values. 3.1 Construction of ds tree.6 UPGMA Method UPGMA stands for Unweighted Pair Group Method using Arithmetic average. It starts with grouping two taxa having smallest distance between them according to the distant matrix, then new node is added in the midpoint of the two, and the two original taxa put on the tree. The distance from the new node to other nodes will be the arithmetic average. Then by replacing two taxa with the new node is used to obtain a reduced distance matrix. Repeat the same process until all taxa are placed on the tree. The taxon added at the last is the root of the tree. n n i j D, i j, k Di, k D j, k ni n j ni n j 39

3 International Journal of Computer Applications ( ) 3. Flow Chart of present work NCBI Databank Load m rice sequences Load n rice sequences Align sequences by multiple sequence alignment Align sequences by multiple sequence alignment Calculate distance by jukes cantor method Calculate distance by jukes cantor method Construct tree by UPGMA method Construct tree by UPGMA method Construct the Neighbor Joining tree Construct the Neighbor Joining tree Compare trees if having same topology Yes 1 Equal NO Not equal Apply threshold condition Satisfied Yes pruned NO Sequence closely related Construction of final tree 4

4 Evolutionary distance Evolutionary distance International Journal of Computer Applications ( ) 3.3 Construction of dn tree 3.5 Phylogenetic tree of five rice varieties using NJ method Neighbor-Joining Distance Tree of 5 sequences Rice using Jukes-Cantor model Indica cultivar Indica cultivar Cultivar Tetep Oryza sativa Indica Oryza sativa Japonica 3.4 Phylogenetic tree of five rice varieties using UPGMA method 3.6 MSA of five rice varieties UPGMA Distance Tree of 5 sequences of RICE using Jukes-Cantor model Indica cultivar Cultivar Tetep Oryza sativa Indica Indica cultivar Oryza sativa Japonica 41

5 Evolutionary distance Evolutionary distance International Journal of Computer Applications ( ) 3.7 Phylogenetic tree of seven rice varieties 3.9 MSA of seven rice varieties Using UPGMA method UPGMA Distance Tree of 7 sequences of RICE using Jukes-Cantor model Japonica cultivar Indica dynein Indica strain Cultivar Lemont Indica dispersed Cultivar Nipponbare Cultivar aromatic 3.8 Phylogenetic tree of seven rice varieties Using NJ method Neighbor-Joining Distance Tree of 7 sequences of Rice using Jukes-Cantor model 3.1 The final UPGMA tree for twelve rice varieties Cultivar Nipponbare Japonica cultivar Cultivar Lemont Indica dispersed Indica dynein Indica strain Cultivar aromatic 4

6 Evolutionary distance Evolutionary distance Evolutionary Distances International Journal of Computer Applications ( ) 3.11 The final Neighbor joining tree for twelve rice varieties Graph plot for jukes cantor calculated values.5 Neighbor-Joining Distance Tree of 1 sequences of Rice using Jukes-Cantor model Cultivar Tetep Indica strain Cultivar aromatic Oryza sativa Indica Cultivar Lemont Indica dispersed Cultivar Nipponbare Japonica cultivar Indica cultivar Indica dynein Indica cultivar Oryza sativa Japonica Columns 3.1 Multiple Sequence alignment for 3.14 Pruned UPGMA distance tree twelve rice varieties Pruned UPGMA Distance Tree of Rice using Jukes-Cantor model Oryza sativa Japonica Cultivar Lemont Indica dynein Indica dispersed Indica cultivar Cultivar Tetep Oryza sativa Indica Cultivar aromatic Indica strain 43

7 International Journal of Computer Applications ( ) 3.15 Final phylogenetic tree for all varieties 4. CONCLUSIONS Phylogenetic tree construction is a complex yet important problem in the field of bioinformatics. Once constructed, a phylogenetic or evolutionary tree can lend insight into the evolution of different species. While in general the topology in phylogenetic trees represents the relationships between the taxa, assigning scales to edges in the trees could provide extra information on the amount of evolution divergence as well as the time of the divergence. The phylogenetic trees with the scaled edges are called phylograms, while the non-scaled phylogenetic trees are called cladograms. The issue is that for a large number of species the problem grows to a computational complexity that is not easily solved. Unweighted Pair Group Method with Arithmetic Mean (UPGMA) is a clustering method for building phylogenetic trees. Clustering algorithms attempt to repeatedly cluster the data by grouping the closest elements. The result is a rooted tree with original sequences at the leaves whereas the neighbor joining algorithm is essentially a so-called agglomerative hierarchical clustering algorithm: starting from one cluster per sequence, it iteratively merges clusters of sequences (merging the most similar ones first) until a single cluster is obtained. The most frequently used distance methods are cluster based. The major advantages is that they are computationally fast and are therefore capable of handling databases that are deemed to be too large for any other phylogenetic methods. Model is constructed for aligning DNA sequences of different Rice varieties. There are different sequence formats available from which FASTA format is utilized. Jukes- Cantor method is used for finding the Evolutionary distances. Two phylogenetic trees are constructed using UPGMA for different datasets. Then by using the advanced pruning techniques, trees are combined to obtain the final tree for complete dataset. The closely related sequences are extracted based on threshold condition. Two phylogenetic trees are constructed using Neighbor Joining for different datasets. Trees constructed using UPGMA and neighbor joining are compared. Cluster analysis (Hierarchical clustering) is used as data mining model to retrieve the result. The result of this research work is the tree construction of a given sequence with improved accuracy. The overall advantage of all distance based methods has the ability to make use of large number of substitution models to correct distances and these algorithms are computed on the sequences of different Rice varieties. 5. ACKNOWLEDGMENTS I wish to express my sincere gratitude and indebtedness to my Supervisor, Prof. Rajbir Singh (Assoc. Prof. & Head, Department of Information Technology) for his valuable guidance, obliging nature and attention-grabbing views which led to the successful completion of this study. I lack words to express my cordial thanks to the members of Departmental Research Committee (DRC) for their useful comments and constructive suggestions during all the phases of the present study as well as critically going through the manuscript. Words fail to express the deep sense of gratitude towards my family members for their moral and financial support and encouragement without which would not have been able to bring out this thesis. 6. REFERENCES [1] Amanda J. Garris,(5) Genetic Structure and Diversity in Oryza sativa L., Oxford Journals, pp [] Archak S. and Nagaraju J., (7) Computational Prediction of Rice (Oryza sativa) mirna Targets, Genomics Proteomics & Bioinformatics, Vol. 5 No. 3 4, pp [3] Arthur M., () Introduction to bioinformatics, oxford university press, pp. 5-8 [4] Bergeron, B. (3) Bioinformatics Computing, Pearson Education, pp [5] David J. HAND, (1998) Data Mining: Statistics and More?, The American Statistician, Vol. 5, No., pp [6] Gronau I. and Moran S., (7) Optimal Implementations of UPGMA and Other Common Clustering Algorithms, Information Processing Letters, Volume 14, Issue 6, pp.5-1. [7] Jacques Cohen (4) Bioinformatics An Introduction for Computer Scientists, ACM Computing Surveys, Vol. 36, No., pp [8] Jose C. Clemente et al., (6) Phylogenetic reconstruction from non-genomic data Oxford University Press, Vol. 3, pp. e11 e115. [9] Khalid R. (1) Application of Data Mining in Bioinformatics, Indian Journal of Computer Science and Engineering, Vol. 1 No, pp [1] Mai S. Mabrouk et al. (6) BIOINFTool: Bioinformatics and sequence data analysis in molecular biology using Matlab, proc. cairo international biomedical engineering conference, pp.1-9. [11] Nair Achuthsankar S., Computational Biology & Bioinformatics: A Gentle Overview, Communications of the Computer Society of India, January 7. [1] Rakshit S. et al., (7) Large-scale DNA polymorphism study of Oryza sativa and O.rufipogon reveals the origin an divergence of Asian rice, Springer, pp [13] Rani S. and Kaur S. (1) Cluster Analysis Method for Multiple Sequence Alignment, International Journal of Computer Applications, Vol. 43 No.14, pp

8 International Journal of Computer Applications ( ) [14] Singh Harmandeep (13) Implementing Hierarchical Clustering method For Multiple Sequence Alignment and Phylogenetic Tree Construction, International Journal of Computer Science, Engineering and Information Technology, Vol.3, No.1, pp [15] Usama Fayyad et al., (1996) From Data Mining to Knowledge Discovery in Databases, American Association for Artificial Intelligence, Volume 17 Number 3, pp AUTHOR S PROFILE Rajbir Singh is an Associate Professor & Head, Department of Information Technology of Lala Lajpat Rai Institute of Engineering & Technology Moga India. He received his B.E (Honor) degree in Computer Science and Engineering from MD University, Rothak, Haryana and M-Tech degree in Computer Science and Engineering from Punjab Technical University, Jalandhar Pb. (INDIA). He has authored 3 books on Computer Science. His main field of research interest is Bio-Informatics and Data mining. He works on the Gene Expression, Phylogenetic Trees and Prediction of Protein Sequence & Structure. Sinapreet Kaur is student of M.Tech Department of Computer Science & Engg., Lala Lajpat Rai institute of engg. & Tech, Moga Punjab, INDIA. She received her B.Tech degree in Information and Technology from Punjab Technical University, Jalandhar, Pb. (INDIA). Her research interest includes Bio-Informatics. She worked on the phylogenetic tree generation using different scoring methods. Dheerajpal Kaur is a Faculty with the Department of Electronics & Communication Engineering of Lala Lajpat Rai Institute of Engineering & Technology Moga, India. She received her B.E in Electronics & Communication Engineering and M-Tech degree in Electronics & Communication Engineering from Punjab Technical University, Jalandhar Pb. (INDIA). Her research interests include Neural Networks, Genetics Algorithm and Data Mining. She works on the Antenna Propagation using Neural Networks in MAT Lab. IJCA TM : 45

MULTIPLE SEQUENCE ALIGNMENT FOR CONSTRUCTION OF PHYLOGENETIC TREE

MULTIPLE SEQUENCE ALIGNMENT FOR CONSTRUCTION OF PHYLOGENETIC TREE MULTIPLE SEQUENCE ALIGNMENT FOR CONSTRUCTION OF PHYLOGENETIC TREE Manmeet Kaur 1, Navneet Kaur Bawa 2 1 M-tech research scholar (CSE Dept) ACET, Manawala,Asr 2 Associate Professor (CSE Dept) ACET, Manawala,Asr

More information

IMPLEMENTING HIERARCHICAL CLUSTERING METHOD FOR MULTIPLE SEQUENCE ALIGNMENT AND PHYLOGENETIC TREE CONSTRUCTION

IMPLEMENTING HIERARCHICAL CLUSTERING METHOD FOR MULTIPLE SEQUENCE ALIGNMENT AND PHYLOGENETIC TREE CONSTRUCTION IMPLEMENTING HIERARCHICAL CLUSTERING METHOD FOR MULTIPLE SEQUENCE ALIGNMENT AND PHYLOGENETIC TREE CONSTRUCTION Harmandeep Singh 1, Er. Rajbir Singh Associate Prof. 2, Navjot Kaur 3 1 Lala Lajpat Rai Institute

More information

THEORY. Based on sequence Length According to the length of sequence being compared it is of following two types

THEORY. Based on sequence Length According to the length of sequence being compared it is of following two types Exp 11- THEORY Sequence Alignment is a process of aligning two sequences to achieve maximum levels of identity between them. This help to derive functional, structural and evolutionary relationships between

More information

Amira A. AL-Hosary PhD of infectious diseases Department of Animal Medicine (Infectious Diseases) Faculty of Veterinary Medicine Assiut

Amira A. AL-Hosary PhD of infectious diseases Department of Animal Medicine (Infectious Diseases) Faculty of Veterinary Medicine Assiut Amira A. AL-Hosary PhD of infectious diseases Department of Animal Medicine (Infectious Diseases) Faculty of Veterinary Medicine Assiut University-Egypt Phylogenetic analysis Phylogenetic Basics: Biological

More information

Dr. Amira A. AL-Hosary

Dr. Amira A. AL-Hosary Phylogenetic analysis Amira A. AL-Hosary PhD of infectious diseases Department of Animal Medicine (Infectious Diseases) Faculty of Veterinary Medicine Assiut University-Egypt Phylogenetic Basics: Biological

More information

"Nothing in biology makes sense except in the light of evolution Theodosius Dobzhansky

Nothing in biology makes sense except in the light of evolution Theodosius Dobzhansky MOLECULAR PHYLOGENY "Nothing in biology makes sense except in the light of evolution Theodosius Dobzhansky EVOLUTION - theory that groups of organisms change over time so that descendeants differ structurally

More information

EVOLUTIONARY DISTANCES

EVOLUTIONARY DISTANCES EVOLUTIONARY DISTANCES FROM STRINGS TO TREES Luca Bortolussi 1 1 Dipartimento di Matematica ed Informatica Università degli studi di Trieste luca@dmi.units.it Trieste, 14 th November 2007 OUTLINE 1 STRINGS:

More information

Phylogenetic Trees. What They Are Why We Do It & How To Do It. Presented by Amy Harris Dr Brad Morantz

Phylogenetic Trees. What They Are Why We Do It & How To Do It. Presented by Amy Harris Dr Brad Morantz Phylogenetic Trees What They Are Why We Do It & How To Do It Presented by Amy Harris Dr Brad Morantz Overview What is a phylogenetic tree Why do we do it How do we do it Methods and programs Parallels

More information

Phylogenetic Tree Reconstruction

Phylogenetic Tree Reconstruction I519 Introduction to Bioinformatics, 2011 Phylogenetic Tree Reconstruction Yuzhen Ye (yye@indiana.edu) School of Informatics & Computing, IUB Evolution theory Speciation Evolution of new organisms is driven

More information

Bio 1B Lecture Outline (please print and bring along) Fall, 2007

Bio 1B Lecture Outline (please print and bring along) Fall, 2007 Bio 1B Lecture Outline (please print and bring along) Fall, 2007 B.D. Mishler, Dept. of Integrative Biology 2-6810, bmishler@berkeley.edu Evolution lecture #5 -- Molecular genetics and molecular evolution

More information

Computational Biology: Basics & Interesting Problems

Computational Biology: Basics & Interesting Problems Computational Biology: Basics & Interesting Problems Summary Sources of information Biological concepts: structure & terminology Sequencing Gene finding Protein structure prediction Sources of information

More information

InDel 3-5. InDel 8-9. InDel 3-5. InDel 8-9. InDel InDel 8-9

InDel 3-5. InDel 8-9. InDel 3-5. InDel 8-9. InDel InDel 8-9 Lecture 5 Alignment I. Introduction. For sequence data, the process of generating an alignment establishes positional homologies; that is, alignment provides the identification of homologous phylogenetic

More information

Phylogenetic analyses. Kirsi Kostamo

Phylogenetic analyses. Kirsi Kostamo Phylogenetic analyses Kirsi Kostamo The aim: To construct a visual representation (a tree) to describe the assumed evolution occurring between and among different groups (individuals, populations, species,

More information

Phylogenetic inference

Phylogenetic inference Phylogenetic inference Bas E. Dutilh Systems Biology: Bioinformatic Data Analysis Utrecht University, March 7 th 016 After this lecture, you can discuss (dis-) advantages of different information types

More information

08/21/2017 BLAST. Multiple Sequence Alignments: Clustal Omega

08/21/2017 BLAST. Multiple Sequence Alignments: Clustal Omega BLAST Multiple Sequence Alignments: Clustal Omega What does basic BLAST do (e.g. what is input sequence and how does BLAST look for matches?) Susan Parrish McDaniel College Multiple Sequence Alignments

More information

Phylogenetic Analysis. Han Liang, Ph.D. Assistant Professor of Bioinformatics and Computational Biology UT MD Anderson Cancer Center

Phylogenetic Analysis. Han Liang, Ph.D. Assistant Professor of Bioinformatics and Computational Biology UT MD Anderson Cancer Center Phylogenetic Analysis Han Liang, Ph.D. Assistant Professor of Bioinformatics and Computational Biology UT MD Anderson Cancer Center Outline Basic Concepts Tree Construction Methods Distance-based methods

More information

POPULATION GENETICS Winter 2005 Lecture 17 Molecular phylogenetics

POPULATION GENETICS Winter 2005 Lecture 17 Molecular phylogenetics POPULATION GENETICS Winter 2005 Lecture 17 Molecular phylogenetics - in deriving a phylogeny our goal is simply to reconstruct the historical relationships between a group of taxa. - before we review the

More information

Phylogenetic Trees. Phylogenetic Trees Five. Phylogeny: Inference Tool. Phylogeny Terminology. Picture of Last Quagga. Importance of Phylogeny 5.

Phylogenetic Trees. Phylogenetic Trees Five. Phylogeny: Inference Tool. Phylogeny Terminology. Picture of Last Quagga. Importance of Phylogeny 5. Five Sami Khuri Department of Computer Science San José State University San José, California, USA sami.khuri@sjsu.edu v Distance Methods v Character Methods v Molecular Clock v UPGMA v Maximum Parsimony

More information

MATHEMATICAL MODELS - Vol. III - Mathematical Modeling and the Human Genome - Hilary S. Booth MATHEMATICAL MODELING AND THE HUMAN GENOME

MATHEMATICAL MODELS - Vol. III - Mathematical Modeling and the Human Genome - Hilary S. Booth MATHEMATICAL MODELING AND THE HUMAN GENOME MATHEMATICAL MODELING AND THE HUMAN GENOME Hilary S. Booth Australian National University, Australia Keywords: Human genome, DNA, bioinformatics, sequence analysis, evolution. Contents 1. Introduction:

More information

Algorithms in Bioinformatics

Algorithms in Bioinformatics Algorithms in Bioinformatics Sami Khuri Department of Computer Science San José State University San José, California, USA khuri@cs.sjsu.edu www.cs.sjsu.edu/faculty/khuri Distance Methods Character Methods

More information

Bioinformatics 1. Sepp Hochreiter. Biology, Sequences, Phylogenetics Part 4. Bioinformatics 1: Biology, Sequences, Phylogenetics

Bioinformatics 1. Sepp Hochreiter. Biology, Sequences, Phylogenetics Part 4. Bioinformatics 1: Biology, Sequences, Phylogenetics Bioinformatics 1 Biology, Sequences, Phylogenetics Part 4 Sepp Hochreiter Klausur Mo. 30.01.2011 Zeit: 15:30 17:00 Raum: HS14 Anmeldung Kusss Contents Methods and Bootstrapping of Maximum Methods Methods

More information

Effects of Gap Open and Gap Extension Penalties

Effects of Gap Open and Gap Extension Penalties Brigham Young University BYU ScholarsArchive All Faculty Publications 200-10-01 Effects of Gap Open and Gap Extension Penalties Hyrum Carroll hyrumcarroll@gmail.com Mark J. Clement clement@cs.byu.edu See

More information

Constructing Evolutionary/Phylogenetic Trees

Constructing Evolutionary/Phylogenetic Trees Constructing Evolutionary/Phylogenetic Trees 2 broad categories: istance-based methods Ultrametric Additive: UPGMA Transformed istance Neighbor-Joining Character-based Maximum Parsimony Maximum Likelihood

More information

Phylogenetics: Building Phylogenetic Trees

Phylogenetics: Building Phylogenetic Trees 1 Phylogenetics: Building Phylogenetic Trees COMP 571 Luay Nakhleh, Rice University 2 Four Questions Need to be Answered What data should we use? Which method should we use? Which evolutionary model should

More information

A (short) introduction to phylogenetics

A (short) introduction to phylogenetics A (short) introduction to phylogenetics Thibaut Jombart, Marie-Pauline Beugin MRC Centre for Outbreak Analysis and Modelling Imperial College London Genetic data analysis with PR Statistics, Millport Field

More information

Bioinformatics. Dept. of Computational Biology & Bioinformatics

Bioinformatics. Dept. of Computational Biology & Bioinformatics Bioinformatics Dept. of Computational Biology & Bioinformatics 3 Bioinformatics - play with sequences & structures Dept. of Computational Biology & Bioinformatics 4 ORGANIZATION OF LIFE ROLE OF BIOINFORMATICS

More information

Copyright 2000 N. AYDIN. All rights reserved. 1

Copyright 2000 N. AYDIN. All rights reserved. 1 Introduction to Bioinformatics Prof. Dr. Nizamettin AYDIN naydin@yildiz.edu.tr Multiple Sequence Alignment Outline Multiple sequence alignment introduction to msa methods of msa progressive global alignment

More information

Additive distances. w(e), where P ij is the path in T from i to j. Then the matrix [D ij ] is said to be additive.

Additive distances. w(e), where P ij is the path in T from i to j. Then the matrix [D ij ] is said to be additive. Additive distances Let T be a tree on leaf set S and let w : E R + be an edge-weighting of T, and assume T has no nodes of degree two. Let D ij = e P ij w(e), where P ij is the path in T from i to j. Then

More information

Phylogenetics: Building Phylogenetic Trees. COMP Fall 2010 Luay Nakhleh, Rice University

Phylogenetics: Building Phylogenetic Trees. COMP Fall 2010 Luay Nakhleh, Rice University Phylogenetics: Building Phylogenetic Trees COMP 571 - Fall 2010 Luay Nakhleh, Rice University Four Questions Need to be Answered What data should we use? Which method should we use? Which evolutionary

More information

Phylogenetic trees 07/10/13

Phylogenetic trees 07/10/13 Phylogenetic trees 07/10/13 A tree is the only figure to occur in On the Origin of Species by Charles Darwin. It is a graphical representation of the evolutionary relationships among entities that share

More information

Phylogenies Scores for Exhaustive Maximum Likelihood and Parsimony Scores Searches

Phylogenies Scores for Exhaustive Maximum Likelihood and Parsimony Scores Searches Int. J. Bioinformatics Research and Applications, Vol. x, No. x, xxxx Phylogenies Scores for Exhaustive Maximum Likelihood and s Searches Hyrum D. Carroll, Perry G. Ridge, Mark J. Clement, Quinn O. Snell

More information

Multiple Sequence Alignment. Sequences

Multiple Sequence Alignment. Sequences Multiple Sequence Alignment Sequences > YOR020c mstllksaksivplmdrvlvqrikaqaktasglylpe knveklnqaevvavgpgftdangnkvvpqvkvgdqvl ipqfggstiklgnddevilfrdaeilakiakd > crassa mattvrsvksliplldrvlvqrvkaeaktasgiflpe

More information

The Phylogenetic Reconstruction of the Grass Family (Poaceae) Using matk Gene Sequences

The Phylogenetic Reconstruction of the Grass Family (Poaceae) Using matk Gene Sequences The Phylogenetic Reconstruction of the Grass Family (Poaceae) Using matk Gene Sequences by Hongping Liang Dissertation submitted to the Faculty of the Virginia Polytechnic Institute and State University

More information

Lecture Notes: Markov chains

Lecture Notes: Markov chains Computational Genomics and Molecular Biology, Fall 5 Lecture Notes: Markov chains Dannie Durand At the beginning of the semester, we introduced two simple scoring functions for pairwise alignments: a similarity

More information

Phylogenetics: Distance Methods. COMP Spring 2015 Luay Nakhleh, Rice University

Phylogenetics: Distance Methods. COMP Spring 2015 Luay Nakhleh, Rice University Phylogenetics: Distance Methods COMP 571 - Spring 2015 Luay Nakhleh, Rice University Outline Evolutionary models and distance corrections Distance-based methods Evolutionary Models and Distance Correction

More information

Bioinformatics tools for phylogeny and visualization. Yanbin Yin

Bioinformatics tools for phylogeny and visualization. Yanbin Yin Bioinformatics tools for phylogeny and visualization Yanbin Yin 1 Homework assignment 5 1. Take the MAFFT alignment http://cys.bios.niu.edu/yyin/teach/pbb/purdue.cellwall.list.lignin.f a.aln as input and

More information

Evolutionary Tree Analysis. Overview

Evolutionary Tree Analysis. Overview CSI/BINF 5330 Evolutionary Tree Analysis Young-Rae Cho Associate Professor Department of Computer Science Baylor University Overview Backgrounds Distance-Based Evolutionary Tree Reconstruction Character-Based

More information

C3020 Molecular Evolution. Exercises #3: Phylogenetics

C3020 Molecular Evolution. Exercises #3: Phylogenetics C3020 Molecular Evolution Exercises #3: Phylogenetics Consider the following sequences for five taxa 1-5 and the known outgroup O, which has the ancestral states (note that sequence 3 has changed from

More information

Introduction to Bioinformatics Introduction to Bioinformatics

Introduction to Bioinformatics Introduction to Bioinformatics Dr. rer. nat. Gong Jing Cancer Research Center Medicine School of Shandong University 2012.11.09 1 Chapter 4 Phylogenetic Tree 2 Phylogeny Evidence from morphological ( 形态学的 ), biochemical, and gene sequence

More information

Introduction to Bioinformatics Online Course: IBT

Introduction to Bioinformatics Online Course: IBT Introduction to Bioinformatics Online Course: IBT Multiple Sequence Alignment Building Multiple Sequence Alignment Lec1 Building a Multiple Sequence Alignment Learning Outcomes 1- Understanding Why multiple

More information

Tree of Life iological Sequence nalysis Chapter http://tolweb.org/tree/ Phylogenetic Prediction ll organisms on Earth have a common ancestor. ll species are related. The relationship is called a phylogeny

More information

9/30/11. Evolution theory. Phylogenetic Tree Reconstruction. Phylogenetic trees (binary trees) Phylogeny (phylogenetic tree)

9/30/11. Evolution theory. Phylogenetic Tree Reconstruction. Phylogenetic trees (binary trees) Phylogeny (phylogenetic tree) I9 Introduction to Bioinformatics, 0 Phylogenetic ree Reconstruction Yuzhen Ye (yye@indiana.edu) School of Informatics & omputing, IUB Evolution theory Speciation Evolution of new organisms is driven by

More information

Inferring Phylogenetic Trees. Distance Approaches. Representing distances. in rooted and unrooted trees. The distance approach to phylogenies

Inferring Phylogenetic Trees. Distance Approaches. Representing distances. in rooted and unrooted trees. The distance approach to phylogenies Inferring Phylogenetic Trees Distance Approaches Representing distances in rooted and unrooted trees The distance approach to phylogenies given: an n n matrix M where M ij is the distance between taxa

More information

Algorithms in Computational Biology (236522) spring 2008 Lecture #1

Algorithms in Computational Biology (236522) spring 2008 Lecture #1 Algorithms in Computational Biology (236522) spring 2008 Lecture #1 Lecturer: Shlomo Moran, Taub 639, tel 4363 Office hours: 15:30-16:30/by appointment TA: Ilan Gronau, Taub 700, tel 4894 Office hours:??

More information

Theory of Evolution Charles Darwin

Theory of Evolution Charles Darwin Theory of Evolution Charles arwin 858-59: Origin of Species 5 year voyage of H.M.S. eagle (83-36) Populations have variations. Natural Selection & Survival of the fittest: nature selects best adapted varieties

More information

Videos. Bozeman, transcription and translation: https://youtu.be/h3b9arupxzg Crashcourse: Transcription and Translation - https://youtu.

Videos. Bozeman, transcription and translation: https://youtu.be/h3b9arupxzg Crashcourse: Transcription and Translation - https://youtu. Translation Translation Videos Bozeman, transcription and translation: https://youtu.be/h3b9arupxzg Crashcourse: Transcription and Translation - https://youtu.be/itsb2sqr-r0 Translation Translation The

More information

Bioinformatics 1 -- lecture 9. Phylogenetic trees Distance-based tree building Parsimony

Bioinformatics 1 -- lecture 9. Phylogenetic trees Distance-based tree building Parsimony ioinformatics -- lecture 9 Phylogenetic trees istance-based tree building Parsimony (,(,(,))) rees can be represented in "parenthesis notation". Each set of parentheses represents a branch-point (bifurcation),

More information

Phylogenetics. BIOL 7711 Computational Bioscience

Phylogenetics. BIOL 7711 Computational Bioscience Consortium for Comparative Genomics! University of Colorado School of Medicine Phylogenetics BIOL 7711 Computational Bioscience Biochemistry and Molecular Genetics Computational Bioscience Program Consortium

More information

Application of Associative Matrices to Recognize DNA Sequences in Bioinformatics

Application of Associative Matrices to Recognize DNA Sequences in Bioinformatics Application of Associative Matrices to Recognize DNA Sequences in Bioinformatics 1. Introduction. Jorge L. Ortiz Department of Electrical and Computer Engineering College of Engineering University of Puerto

More information

SUPPLEMENTARY INFORMATION

SUPPLEMENTARY INFORMATION Supplementary information S3 (box) Methods Methods Genome weighting The currently available collection of archaeal and bacterial genomes has a highly biased distribution of isolates across taxa. For example,

More information

Lecture Notes: BIOL2007 Molecular Evolution

Lecture Notes: BIOL2007 Molecular Evolution Lecture Notes: BIOL2007 Molecular Evolution Kanchon Dasmahapatra (k.dasmahapatra@ucl.ac.uk) Introduction By now we all are familiar and understand, or think we understand, how evolution works on traits

More information

Bioinformatics Chapter 1. Introduction

Bioinformatics Chapter 1. Introduction Bioinformatics Chapter 1. Introduction Outline! Biological Data in Digital Symbol Sequences! Genomes Diversity, Size, and Structure! Proteins and Proteomes! On the Information Content of Biological Sequences!

More information

Investigation 3: Comparing DNA Sequences to Understand Evolutionary Relationships with BLAST

Investigation 3: Comparing DNA Sequences to Understand Evolutionary Relationships with BLAST Investigation 3: Comparing DNA Sequences to Understand Evolutionary Relationships with BLAST Introduction Bioinformatics is a powerful tool which can be used to determine evolutionary relationships and

More information

Module: Sequence Alignment Theory and Applications Session: Introduction to Searching and Sequence Alignment

Module: Sequence Alignment Theory and Applications Session: Introduction to Searching and Sequence Alignment Module: Sequence Alignment Theory and Applications Session: Introduction to Searching and Sequence Alignment Introduction to Bioinformatics online course : IBT Jonathan Kayondo Learning Objectives Understand

More information

CS612 - Algorithms in Bioinformatics

CS612 - Algorithms in Bioinformatics Fall 2017 Databases and Protein Structure Representation October 2, 2017 Molecular Biology as Information Science > 12, 000 genomes sequenced, mostly bacterial (2013) > 5x10 6 unique sequences available

More information

SUPPLEMENTARY INFORMATION

SUPPLEMENTARY INFORMATION Supplementary information S1 (box). Supplementary Methods description. Prokaryotic Genome Database Archaeal and bacterial genome sequences were downloaded from the NCBI FTP site (ftp://ftp.ncbi.nlm.nih.gov/genomes/all/)

More information

CHAPTERS 24-25: Evidence for Evolution and Phylogeny

CHAPTERS 24-25: Evidence for Evolution and Phylogeny CHAPTERS 24-25: Evidence for Evolution and Phylogeny 1. For each of the following, indicate how it is used as evidence of evolution by natural selection or shown as an evolutionary trend: a. Paleontology

More information

Using Bioinformatics to Study Evolutionary Relationships Instructions

Using Bioinformatics to Study Evolutionary Relationships Instructions 3 Using Bioinformatics to Study Evolutionary Relationships Instructions Student Researcher Background: Making and Using Multiple Sequence Alignments One of the primary tasks of genetic researchers is comparing

More information

Quantifying sequence similarity

Quantifying sequence similarity Quantifying sequence similarity Bas E. Dutilh Systems Biology: Bioinformatic Data Analysis Utrecht University, February 16 th 2016 After this lecture, you can define homology, similarity, and identity

More information

Constructing Evolutionary/Phylogenetic Trees

Constructing Evolutionary/Phylogenetic Trees Constructing Evolutionary/Phylogenetic Trees 2 broad categories: Distance-based methods Ultrametric Additive: UPGMA Transformed Distance Neighbor-Joining Character-based Maximum Parsimony Maximum Likelihood

More information

Phylogeny Tree Algorithms

Phylogeny Tree Algorithms Phylogeny Tree lgorithms Jianlin heng, PhD School of Electrical Engineering and omputer Science University of entral Florida 2006 Free for academic use. opyright @ Jianlin heng & original sources for some

More information

BINF6201/8201. Molecular phylogenetic methods

BINF6201/8201. Molecular phylogenetic methods BINF60/80 Molecular phylogenetic methods 0-7-06 Phylogenetics Ø According to the evolutionary theory, all life forms on this planet are related to one another by descent. Ø Traditionally, phylogenetics

More information

Comparative genomics: Overview & Tools + MUMmer algorithm

Comparative genomics: Overview & Tools + MUMmer algorithm Comparative genomics: Overview & Tools + MUMmer algorithm Urmila Kulkarni-Kale Bioinformatics Centre University of Pune, Pune 411 007. urmila@bioinfo.ernet.in Genome sequence: Fact file 1995: The first

More information

Sara C. Madeira. Universidade da Beira Interior. (Thanks to Ana Teresa Freitas, IST for useful resources on this subject)

Sara C. Madeira. Universidade da Beira Interior. (Thanks to Ana Teresa Freitas, IST for useful resources on this subject) Bioinformática Sequence Alignment Pairwise Sequence Alignment Universidade da Beira Interior (Thanks to Ana Teresa Freitas, IST for useful resources on this subject) 1 16/3/29 & 23/3/29 27/4/29 Outline

More information

Sequence Alignment: A General Overview. COMP Fall 2010 Luay Nakhleh, Rice University

Sequence Alignment: A General Overview. COMP Fall 2010 Luay Nakhleh, Rice University Sequence Alignment: A General Overview COMP 571 - Fall 2010 Luay Nakhleh, Rice University Life through Evolution All living organisms are related to each other through evolution This means: any pair of

More information

Incremental Phylogenetics by Repeated Insertions: An Evolutionary Tree Algorithm

Incremental Phylogenetics by Repeated Insertions: An Evolutionary Tree Algorithm Incremental Phylogenetics by Repeated Insertions: An Evolutionary Tree Algorithm Peter Z. Revesz, Zhiqiang Li Abstract We introduce the idea of constructing hypothetical evolutionary trees using an incremental

More information

(Lys), resulting in translation of a polypeptide without the Lys amino acid. resulting in translation of a polypeptide without the Lys amino acid.

(Lys), resulting in translation of a polypeptide without the Lys amino acid. resulting in translation of a polypeptide without the Lys amino acid. 1. A change that makes a polypeptide defective has been discovered in its amino acid sequence. The normal and defective amino acid sequences are shown below. Researchers are attempting to reproduce the

More information

GENETICS - CLUTCH CH.22 EVOLUTIONARY GENETICS.

GENETICS - CLUTCH CH.22 EVOLUTIONARY GENETICS. !! www.clutchprep.com CONCEPT: OVERVIEW OF EVOLUTION Evolution is a process through which variation in individuals makes it more likely for them to survive and reproduce There are principles to the theory

More information

Phylogeny: building the tree of life

Phylogeny: building the tree of life Phylogeny: building the tree of life Dr. Fayyaz ul Amir Afsar Minhas Department of Computer and Information Sciences Pakistan Institute of Engineering & Applied Sciences PO Nilore, Islamabad, Pakistan

More information

3. SEQUENCE ANALYSIS BIOINFORMATICS COURSE MTAT

3. SEQUENCE ANALYSIS BIOINFORMATICS COURSE MTAT 3. SEQUENCE ANALYSIS BIOINFORMATICS COURSE MTAT.03.239 25.09.2012 SEQUENCE ANALYSIS IS IMPORTANT FOR... Prediction of function Gene finding the process of identifying the regions of genomic DNA that encode

More information

C.DARWIN ( )

C.DARWIN ( ) C.DARWIN (1809-1882) LAMARCK Each evolutionary lineage has evolved, transforming itself, from a ancestor appeared by spontaneous generation DARWIN All organisms are historically interconnected. Their relationships

More information

NJMerge: A generic technique for scaling phylogeny estimation methods and its application to species trees

NJMerge: A generic technique for scaling phylogeny estimation methods and its application to species trees NJMerge: A generic technique for scaling phylogeny estimation methods and its application to species trees Erin Molloy and Tandy Warnow {emolloy2, warnow}@illinois.edu University of Illinois at Urbana

More information

Comparing whole genomes

Comparing whole genomes BioNumerics Tutorial: Comparing whole genomes 1 Aim The Chromosome Comparison window in BioNumerics has been designed for large-scale comparison of sequences of unlimited length. In this tutorial you will

More information

Organizing Life s Diversity

Organizing Life s Diversity 17 Organizing Life s Diversity section 2 Modern Classification Classification systems have changed over time as information has increased. What You ll Learn species concepts methods to reveal phylogeny

More information

Probabilistic modeling and molecular phylogeny

Probabilistic modeling and molecular phylogeny Probabilistic modeling and molecular phylogeny Anders Gorm Pedersen Molecular Evolution Group Center for Biological Sequence Analysis Technical University of Denmark (DTU) What is a model? Mathematical

More information

Algorithms in Bioinformatics FOUR Pairwise Sequence Alignment. Pairwise Sequence Alignment. Convention: DNA Sequences 5. Sequence Alignment

Algorithms in Bioinformatics FOUR Pairwise Sequence Alignment. Pairwise Sequence Alignment. Convention: DNA Sequences 5. Sequence Alignment Algorithms in Bioinformatics FOUR Sami Khuri Department of Computer Science San José State University Pairwise Sequence Alignment Homology Similarity Global string alignment Local string alignment Dot

More information

CS5238 Combinatorial methods in bioinformatics 2003/2004 Semester 1. Lecture 8: Phylogenetic Tree Reconstruction: Distance Based - October 10, 2003

CS5238 Combinatorial methods in bioinformatics 2003/2004 Semester 1. Lecture 8: Phylogenetic Tree Reconstruction: Distance Based - October 10, 2003 CS5238 Combinatorial methods in bioinformatics 2003/2004 Semester 1 Lecture 8: Phylogenetic Tree Reconstruction: Distance Based - October 10, 2003 Lecturer: Wing-Kin Sung Scribe: Ning K., Shan T., Xiang

More information

METHODS FOR DETERMINING PHYLOGENY. In Chapter 11, we discovered that classifying organisms into groups was, and still is, a difficult task.

METHODS FOR DETERMINING PHYLOGENY. In Chapter 11, we discovered that classifying organisms into groups was, and still is, a difficult task. Chapter 12 (Strikberger) Molecular Phylogenies and Evolution METHODS FOR DETERMINING PHYLOGENY In Chapter 11, we discovered that classifying organisms into groups was, and still is, a difficult task. Modern

More information

Taxonomy. Content. How to determine & classify a species. Phylogeny and evolution

Taxonomy. Content. How to determine & classify a species. Phylogeny and evolution Taxonomy Content Why Taxonomy? How to determine & classify a species Domains versus Kingdoms Phylogeny and evolution Why Taxonomy? Classification Arrangement in groups or taxa (taxon = group) Nomenclature

More information

CREATING PHYLOGENETIC TREES FROM DNA SEQUENCES

CREATING PHYLOGENETIC TREES FROM DNA SEQUENCES INTRODUCTION CREATING PHYLOGENETIC TREES FROM DNA SEQUENCES This worksheet complements the Click and Learn developed in conjunction with the 2011 Holiday Lectures on Science, Bones, Stones, and Genes:

More information

Tools and Algorithms in Bioinformatics

Tools and Algorithms in Bioinformatics Tools and Algorithms in Bioinformatics GCBA815, Fall 2015 Week-4 BLAST Algorithm Continued Multiple Sequence Alignment Babu Guda, Ph.D. Department of Genetics, Cell Biology & Anatomy Bioinformatics and

More information

A Phylogenetic Network Construction due to Constrained Recombination

A Phylogenetic Network Construction due to Constrained Recombination A Phylogenetic Network Construction due to Constrained Recombination Mohd. Abdul Hai Zahid Research Scholar Research Supervisors: Dr. R.C. Joshi Dr. Ankush Mittal Department of Electronics and Computer

More information

Biological Networks: Comparison, Conservation, and Evolution via Relative Description Length By: Tamir Tuller & Benny Chor

Biological Networks: Comparison, Conservation, and Evolution via Relative Description Length By: Tamir Tuller & Benny Chor Biological Networks:,, and via Relative Description Length By: Tamir Tuller & Benny Chor Presented by: Noga Grebla Content of the presentation Presenting the goals of the research Reviewing basic terms

More information

Cellular Automata Evolution for Pattern Recognition

Cellular Automata Evolution for Pattern Recognition Cellular Automata Evolution for Pattern Recognition Pradipta Maji Center for Soft Computing Research Indian Statistical Institute, Kolkata, 700 108, INDIA Under the supervision of Prof. P Pal Chaudhuri

More information

Bioinformatics Exercises

Bioinformatics Exercises Bioinformatics Exercises AP Biology Teachers Workshop Susan Cates, Ph.D. Evolution of Species Phylogenetic Trees show the relatedness of organisms Common Ancestor (Root of the tree) 1 Rooted vs. Unrooted

More information

BIOINFORMATICS: An Introduction

BIOINFORMATICS: An Introduction BIOINFORMATICS: An Introduction What is Bioinformatics? The term was first coined in 1988 by Dr. Hwa Lim The original definition was : a collective term for data compilation, organisation, analysis and

More information

8/23/2014. Phylogeny and the Tree of Life

8/23/2014. Phylogeny and the Tree of Life Phylogeny and the Tree of Life Chapter 26 Objectives Explain the following characteristics of the Linnaean system of classification: a. binomial nomenclature b. hierarchical classification List the major

More information

PHYLOGENY AND SYSTEMATICS

PHYLOGENY AND SYSTEMATICS AP BIOLOGY EVOLUTION/HEREDITY UNIT Unit 1 Part 11 Chapter 26 Activity #15 NAME DATE PERIOD PHYLOGENY AND SYSTEMATICS PHYLOGENY Evolutionary history of species or group of related species SYSTEMATICS Study

More information

Consistency Index (CI)

Consistency Index (CI) Consistency Index (CI) minimum number of changes divided by the number required on the tree. CI=1 if there is no homoplasy negatively correlated with the number of species sampled Retention Index (RI)

More information

Estimating Phylogenies (Evolutionary Trees) II. Biol4230 Thurs, March 2, 2017 Bill Pearson Jordan 6-057

Estimating Phylogenies (Evolutionary Trees) II. Biol4230 Thurs, March 2, 2017 Bill Pearson Jordan 6-057 Estimating Phylogenies (Evolutionary Trees) II Biol4230 Thurs, March 2, 2017 Bill Pearson wrp@virginia.edu 4-2818 Jordan 6-057 Tree estimation strategies: Parsimony?no model, simply count minimum number

More information

Understanding relationship between homologous sequences

Understanding relationship between homologous sequences Molecular Evolution Molecular Evolution How and when were genes and proteins created? How old is a gene? How can we calculate the age of a gene? How did the gene evolve to the present form? What selective

More information

Grundlagen der Bioinformatik Summer semester Lecturer: Prof. Daniel Huson

Grundlagen der Bioinformatik Summer semester Lecturer: Prof. Daniel Huson Grundlagen der Bioinformatik, SS 10, D. Huson, April 12, 2010 1 1 Introduction Grundlagen der Bioinformatik Summer semester 2010 Lecturer: Prof. Daniel Huson Office hours: Thursdays 17-18h (Sand 14, C310a)

More information

Molecular phylogeny - Using molecular sequences to infer evolutionary relationships. Tore Samuelsson Feb 2016

Molecular phylogeny - Using molecular sequences to infer evolutionary relationships. Tore Samuelsson Feb 2016 Molecular phylogeny - Using molecular sequences to infer evolutionary relationships Tore Samuelsson Feb 2016 Molecular phylogeny is being used in the identification and characterization of new pathogens,

More information

Open a Word document to record answers to any italicized questions. You will the final document to me at

Open a Word document to record answers to any italicized questions. You will  the final document to me at Molecular Evidence for Evolution Open a Word document to record answers to any italicized questions. You will email the final document to me at tchnsci@yahoo.com Pre Lab Activity: Genes code for amino

More information

Map of AP-Aligned Bio-Rad Kits with Learning Objectives

Map of AP-Aligned Bio-Rad Kits with Learning Objectives Map of AP-Aligned Bio-Rad Kits with Learning Objectives Cover more than one AP Biology Big Idea with these AP-aligned Bio-Rad kits. Big Idea 1 Big Idea 2 Big Idea 3 Big Idea 4 ThINQ! pglo Transformation

More information

Phylogeny Estimation and Hypothesis Testing using Maximum Likelihood

Phylogeny Estimation and Hypothesis Testing using Maximum Likelihood Phylogeny Estimation and Hypothesis Testing using Maximum Likelihood For: Prof. Partensky Group: Jimin zhu Rama Sharma Sravanthi Polsani Xin Gong Shlomit klopman April. 7. 2003 Table of Contents Introduction...3

More information

Biology 559R: Introduction to Phylogenetic Comparative Methods Topics for this week (Jan 27 & 29):

Biology 559R: Introduction to Phylogenetic Comparative Methods Topics for this week (Jan 27 & 29): Biology 559R: Introduction to Phylogenetic Comparative Methods Topics for this week (Jan 27 & 29): Statistical estimation of models of sequence evolution Phylogenetic inference using maximum likelihood:

More information

9/19/2012. Chapter 17 Organizing Life s Diversity. Early Systems of Classification

9/19/2012. Chapter 17 Organizing Life s Diversity. Early Systems of Classification Section 1: The History of Classification Section 2: Modern Classification Section 3: Domains and Kingdoms Click on a lesson name to select. Early Systems of Classification Biologists use a system of classification

More information

OMICS Journals are welcoming Submissions

OMICS Journals are welcoming Submissions OMICS Journals are welcoming Submissions OMICS International welcomes submissions that are original and technically so as to serve both the developing world and developed countries in the best possible

More information

5. MULTIPLE SEQUENCE ALIGNMENT BIOINFORMATICS COURSE MTAT

5. MULTIPLE SEQUENCE ALIGNMENT BIOINFORMATICS COURSE MTAT 5. MULTIPLE SEQUENCE ALIGNMENT BIOINFORMATICS COURSE MTAT.03.239 03.10.2012 ALIGNMENT Alignment is the task of locating equivalent regions of two or more sequences to maximize their similarity. Homology:

More information